The work deals with molecular dynamics (MD) simulationsof protonated,human telomeric i-motif deoxyribonucleic acid (DNA) with functionalizedgraphene. We studied three different graphene sheets: unmodified graphenewith hydrogen atoms attached to their edges and two functionalizedones. The functionalization of graphene edge consists in attachingpartially protonated or dissociated amine and carboxyl groups. Wefound that in all cases the protonated i-motif adsorbs strongly onthe graphene surface. The biased MD simulations showed that the worknecessary to drag the i-motif out from amine-doped graphene is abouttwice larger than that in other cases. In general, the system i-motif/amine-dopedgraphene stands out from the rest, e.g., in this case, the i-motifadsorbs its side with 3′ and 5′ ends oriented in theopposite to surface direction. In other cases, the DNA fragment isadsorbed to graphene by 3′ and 5′ ends. In all cases,the adsorption on graphene influences the i-motif internal structureby changing the distances between i-motif strands as well as stretchingor shortening the DNA chain, but only in the case of amine-doped graphenethe adsorption affects internal H-bonds formed between nucleotidesinside the i-motif structure. It contains information necessary for the reproduction, growth,and functioning of living organisms. In the sequence of nucleotides,DNA has genetic instructions with information necessary for the synthesisof all proteins. Moreover, each strand of DNA in the double helixcan serve as a pattern for duplicating itself. DNA molecules are verylong and are packed into more complex structures called chromosomes.An eukaryotic, i.e., human chromosome, is terminated by a specificsequence of bases called telomere which is normally folded at itsvery end into a loop. Inside chromosomes, DNA is coiled around histonesmainly in a double-helix form. Moreover, a wide spatial versatilityof DNA has been observed, like A-DNA, B-DNA, or Z-DNA in additionto the typical Watson–Crick duplex DNA. Also, a number of alternativestructures including four-stranded G-quadruplex and intercalated i-motifDNA have been found.n sequences,an individual DNA strand is ableto form a quadruplex structure. When the DNA region is cytosine-richit may fold into four-stranded, i-motif structure. It has been discoveredthat a telomeric i-motif can occur in vivo and plays an importantrole in the control of gene expression.5In the region of telomericDNA composed of (TTAGGG/CCCTAA)+, Na+, or K+ in solution.In addition, a few nanosized carbon materials can work as a stabilizerof the spatial i-motif structure.9 Due to its adsorption properties and sensitivityto pH, the i-motif can be considered as a bioconjugation factor8 and biosensor component.It is commonlyassumed that the i-motif structure is stabilizedat acidic pH. The stabilizing effect enhances the presence of monovalentions like Ag11 Almost unlimitedpossibilities of the practical use of DNA interactions with nanotubeshave been proposed.12The interactionof DNA structures with carbon nanomaterials hasgained increasing interest in the literature in the past few years.It concerns the interactions of carbon nanomaterials (CNMs) with differentstructures of DNA, e.g., single-stranded, duplex, triplex, and quadruplexones. As an example, it has been observed that single- and double-strandednucleic acids can adsorb onto carbon nanotubes. Single-stranded DNAcan helically wrap around a single-walled nanotube. In the case ofdouble-stranded DNA, its adsorption at the nanotube is weak. Nevertheless,it was reported that double-stranded DNA wrapped around nanotube isprotected from enzymatic cleavage.13 The binding affinity of nucleic acid to graphene oxideis dependent on the presence of a single-stranded region.14 The adsorption/desorption event of nucleic acidon nanomaterials is one of the key steps for effective DNA and RNAisolation.16In additionto carbon nanotubes, graphene is also the subject ofintense research. Most often it relates to graphene oxide which isconsidered as the component of biosensors, drugs, and gene nanocarriers.Similar to carbon nanotubes, single-stranded DNA shows stronger adsorptionaffinity to graphene. It has been found that cations, pH, and organicsolvent influence DNA binding to graphene oxide.17 Also, double-stranded DNA can adsorb at oxidized graphenebut in high ionic strength solutions. One of the most interestingapplications of graphene-DNA systems is molecular computing.18 Such biomolecular systems can play a role inlogic gates.19 Due to the outstanding physicalcharacteristics for DNA sequencing, graphene can also be consideredas a key component of modern virus tests.20Moreover, in the case of graphene oxide, it has been observedthatthe binding affinity depends on the DNA sequence.The aim of this study is a detailed analysis of the i-motifinteractionand adsorption on an edge functionalized graphene layer. We will investigatehow the functionalization style influences the i-motif adsorptionand how adsorption affects the i-motif spatial structure. In addition,we will carefully examine the physical properties of the investigatedsystems.221 and employing generalized amber force field(GAFF)22 for the functionalized graphenesheet and parm99 Amber force field with bsc1 modifications for thessDNA fragment.23 The structure of thei-motif considered in this study has been obtained by modifying thestructure published by Phan et al.25 (pdb ID 1EL2).The topology of the protonated i-motif has been built applying AmberTools20 package26 and ACPYPE script.27The studies werecarried out using Gromacs 2020 simulation suiteDuring simulations, we investigated thespatial configuration ofthe i-motif by monitoring the distances between selected phosphorusatoms from the backbone located at the characteristic points of thestructure, as shown in 28 Thus, we studied the structurein which half of the cytosines are protonated. The sequence of nucleobasesin the DNA chain is then (CCCTAA)2C+C+C+TAAC+C+C+T  charge model and the Gaussian09 quantum mechanics program. Next, the information obtained fromthe R.E.D. Server has been applied to the whole graphene sheets bymanual edition of MOL2 files. Finally, using ACPYPE, we have builtthe force field topology according to the GAFF parameterization. Graphenesheets are almost rectangular and only two opposite edges were functionalized(2), its total chargeis +9, whereas for the carboxyl-doped one (GR-COOH), the total chargeof the sheet is −9. Carbon atoms at the graphene edge are saturatedwith hydrogen atoms.The functionalization of graphene sheets assumedattaching amineand carboxyl groups to their edges. Half of them are in charged form:amine groups are protonated and carboxyl groups are dissociated. Thesefunctional groups are present in a lot of biological systems. Becauseof the large number of atoms in graphene sheets applied in the presentstudies , only theircharacteristic fragments were used to build topologies. These characteristicfragments included about 50 atoms and were about 9 nm × 9 nmin sizes. This procedure is due to the fact that R.E.D. Serveronalized3. In theNPT ensemblein the cubic box with sizes approximately 11 nm × 11 nm ×11 nm. Boxes were composed of one i-motif and one graphene sheet,about 40,000 tip3p water molecules and a dozen of Na+ andCl– ions to imitate 0.145 mol/dm3 ionicstrength.Simulations were carried out in the 31 or even 1000ns32 but we think that in our case it ispossible to simulate a little shorter. This is confirmed by the valuesof parameters monitored during the simulation, e.g., the root of meansquared displacement (RMSD) of the i-motif from its initial state.As we will show in the work later, after a maximum of several nanoseconds,the values of different parameters monitored during simulations shortlyreach an approximately constant value.The temperature of simulation systems was maintainedat 310 K usinga Nose–Hoover thermostat. Periodic boundary conditions wereapplied. For long-range electrostatic interactions, the smooth particle-meshEwald algorithm was used. Cutoff distances for Lennard–Jonesand Columbic interactions were set to 1.2 nm. All particles were placedrandomly in the simulation box. Equations of motion were integratedusing the Leap-Frog algorithm with the time step of 2 fs. Each simulationwas equilibrated for 10 ps. Based on our previous experience in studyingthe interaction of DNA with carbon nanotubes, e.g., in ref , we deci32 and GR-COOH, which are positively andnegatively charged, respectively, the charge of graphene sheet wherefunctionalization has not been applied is neutral. It could be assumedthat the electric charge of graphene is one of the most importantfactors which influences the interactions with the protonated, i-motifDNA fragment. This thesis confirms the minimum distance between thecenter of mass (COM) of the i-motif structure and the closest atomfrom the graphene surface, calculated for investigated systems. 2 and COM of the i-motif is reached at about 1 ns. In the caseof the system with GR-COOH, this time is 7 ns. For the simulationsystem which includes graphene without functionalization, GR-H, theprocess in which the i-motif approaches the graphene surface is themost time consuming, as it takes approximately 15 ns. Also, the minimumdistance between these two structures depends on the type of graphenefunctionalization. In the case of GR-H and GR-COOH, when the i-motifreaches the graphene surface, the minimum distances between thesetwo structures are initially a bit different. The distance of theCOM of the i-motif and GR-H is ca. 1.8 nm, whereas the distance fromthe GR-COOH surface is about 1.6 nm. After 35.5 ns of simulations,the i-motif molecules get closer to GR-H and reach almost the sameposition as in the i-motif/GR-COOH. It is worth noting that the fluctuationsof distance for both pairs are similar but for the system with GR-NH2 they are a little bigger. After reaching the equilibriumconfiguration, the minimum distances of i-motif/GR-H and i-motif/GR-COOHvary in the range of ca. 0.25 nm, whereas the minimum distance betweenthe DNA fragment and GR-NH2 varies in the range of ca.0.3 nm.The studieswere carried out for three systems that differed inthe method of graphene functionalization. Instead of amine- and carboxyl-dopedgraphene, GR-NHBecause the starting configuration of simulatedsystems was generatedin a random way, the time consumed for the DNA fragment to approachthe graphene plane says little about the physical affinity betweenthese two structures. Nevertheless, it is clearly seen that, in comparisonto other systems, the amine-doped graphene interacts with the i-motifin a different way. Detailed examination of the positions of P1–P5points shown in 2 system, as showed in the part (b) of 2 surface is approximately 0.6 nm, whereasthe analogous distances from GR-H and GR-COOH are approximately 3and 2.7 nm. Further comparison of the distances presented in 2 is oriented “upside down,”i.e., the DNA fragment is adsorbed to graphene with the side containingP2 and P4 atoms. Moreover, the fluctuations of minimum distances shownin 2 dimer is the most stable among the consideredsystems. For the record, P1, P3, and P5 phosphorus atomsare located onthe i-motif’s side with 3′ and 5′ ends, whereasP2 and P4 are on its opposite side. 30 When studying i-motif interactions with single-walledcarbon nanotubes, we also observed its different orientation withrespect to the nanotube surface. While using the same terminology,the arrangement of the i-motif in the case of adsorption on GR-NH2 can be called as “top” orientation, whereasin other cases we have the “bottom” orientation.As seen in One of the most interesting effects is the influence of grapheneplane on the i-motif structure. The quantitative analysis of suchan effect can be done through the investigation of mutual distancesbetween P1, P2, P3, P4, and P5 phosphorus atoms. 2 and decreases when it is adsorbed at GR-COOH . It is dueto the fact that a part of carboxyl groups is in anionic form. Finally,the i-motif structure adsorbed at nonfunctionalized graphene GR-Hin some period of simulation trajectory also reaches the grapheneedge. This is due to charge distribution between carbon atoms in grapheneand hydrogen atoms at its edge. While carbon atoms in the center ofthe sheet have neutral charge, the carbon atoms near the grapheneedges are increasingly negative charged. The total charge of the graphenesheet is balanced by positively charged hydrogen atoms. Thus, whenthe i-motif comes close enough to the edge of the GR-H sheet, it fallsinto a potential well where it is attracted by positively chargedhydrogen atoms that are on that edge.It can be concluded that in the case of GR-NHr and α. One of them is the distance r between two strongly electronegative atoms D and A from hydrogenbond donor D–H and hydrogen bond acceptor A. The second oneis the angle α between three atoms D–H···A.The bond becomes stronger when defined in such a way that distance r and angle α become smaller. Here, we applied themost commonly used values of H-bond parameters: r ≤ 0.35 nm and α ≤ 30°. Thus, when bothcriteria are fulfilled, the H-bond bridge is formed. Figures 13The next feature investigatedis hydrogen bonds between nucleotidesinside the i-motif molecule. It is worth recalling that, in the MDsimulations, hydrogen bonds are defined by two geometrical parameters 2).It is worth noting that only in the case of GR-H,we have a relativelylong simulation trajectory for the nonadsorbed i-motif at disposalsee 4. Inoth2. While H-bond lengthsbetween i-motif nucleobases remain unchanged, their mutual orientationchanges slightly. The most probable H-bond angle changes from about8° to 11°. Although it is not a big change, the shapes ofthe angle distribution curves shown in 33 Nevertheless,angles change in a relatively narrow range from 0° to 20°.A much more narrow range of changes occurs in the case of H-bond distancedistributions. It is always a very narrow peak with the maximum atabout 0.29 nm. These values indicate that hydrogen bonds between nucleotidesinside the i-motif structure have, in most cases, a strong characterwith dominant strongly covalent interactions.What we can seefrom 2, the i-motif adsorbs “upside down,” i.e., withits 3′ and 5′ ends on the opposite side to the graphenesurface . It seems that this effect may mainly be responsiblefor the different properties of the i-motif/GR-NH2 system.In comparisonto other investigated systems, we observed that whenthe i-motif adsorbs at the amine-doped graphene, the fluctuationsof distances between its selected phosphorous atoms are the smallestand, simultaneously, the directionality of i-motif internal H-bondsin this system decreases. Thus, it seems that the influence of amine-dopedgraphene on the stability of the DNA fragment is much more complex.It is worth noting that among the investigated systems, only at GR-NHThus, we can state that the DNA fragment is more strongly adsorbedby amine-doped graphene see 4. Theref34 In such a case, it is necessary to average a lot of trajectoriesdetermined from various initial configurations. Unfortunately, sucha procedure is numerically complicated even for simple systems. Forthis reason, the work profiles shown in 34 ensures that exponential averaging of many work versusdistance/time profiles can lead to the exact free energy profile inthe limit of infinite number of such trajectories determined. Becausewe used only a single trajectory, the obtained profiles are probablyfar from a true free energy of binding. Nevertheless, applicationof the same settings during the enforced detachments allows us tocompare properties of these three systems and draw reliable conclusionsabout their stability.It is worth mentioning that the constant velocitydragging canformally be used to determine the free energy.Nevertheless, the analysis of 2, the required work is approx. twicelarger than in the case of GR-H and GR-COOH. It is about 427 kJ/mol,whereas the work necessary to drag i-motif out from GR-H and GR-COOHis 265 and 252 kJ/mol, respectively. Such results are consistent withour previous observations and confirm the significant role of electrostaticsinteractions in the i-motif/GR-NH2 dimer. Another factorinfluencing the value of this work can be the reverse i-motif orientationon the GR-NH2 surface.First of all, we cansee a huge difference between the work necessaryto remove i-motif from various functionalized graphene surfaces. Inthe case of GR-NHsc of the i-motif draggedout from graphene surfaces.35Regardless of the type offunctionalized graphene, the time necessaryto drag the DNA fragment out is about 3 ns. In the case of i-motif/GR-Hand i-motif/GR-COOH, the extreme  parts of workprofiles are similar. Visible difference can be seen in the rangeof 1.0 to 3.5 ns. It suggests that the spring attached to the i-motifCOM during biased simulations causes not only its desorption, butalso influences the i-motif spatial structure. This is one of thereasons why calculated work is not the same as the free energy ofinteraction. To check it more carefully, in 2. In this case, up to 1 ns of simulation, the similarity parameterρsc varies in the range of 0 to 0.7. Following Maiorovand Crippen,35 when ρsc > 0.5, it can be interpreted as antisimilarity from the initialstructure. Next, when the DNA fragment is dragged out from GR-NH2 surface (times in the range of 1 to 4 ns), the similarityparameter remains constant. Finally, when the i-motif is desorbed,its structure randomly changes during some relaxations connected withshape optimization. In other cases, the similarity parameters ρsc of the i-motif adsorbed on GR-H and GR-COOH are much lower.Their values are in the range of 0.3 to 0.5 and can be interpretedas visually recognizable similarity of the i-motif structure to thereference state.35As can be expected, the spatial structure of thei-motif is modifiedto the greatest extent in the case of dragging it from GR-NHAlthough the mainaim of the paper is the investigation of thechanges in the spatial i-motif structure caused by adsorption, itis worth seeing the effect of DNA adsorption on the graphene sheet. It can be stated that the changes of RMSD are smalland basicallydo not exceed 0.2 nm. This indicates a high degree of similarity ofgraphene sheets to their initial states when DNA was far from thesurface. The changes in RMSD are caused by thermal fluctuations. Itis worth noticing that RMSD plots shown in 42 system is much more different from the rest. In the caseof i-motif adsorption on GR-H and GR-COOH, the DNA fragment orientedits 3′ and 5′ ends toward graphene, whereas on the GR-NH2 surface it is inversely oriented.The presented studies enableto recognize new physical insightsinto the interaction of the i-motif with functionalized graphene.First of all, the i-motif is very stably bonded to the graphene surface.The minimum distance between these two structures depends on the typeof graphene functionalization, but the shortest one is in the caseof amine-doped graphene. Further analysis showed that the i-motif/GR-NH2 causes the stabilization of the upstream part ofthe DNA strand, and the mobility of thymine and adenines is very low.In the case of GR-H and GR-COOH, the opposite effect is observed.While consideringthe influence of graphene plane on the i-motifstructure, our investigations suggest the changes of its internalstructure due to adsorption interactions. Such changes consist ofthe stretching or shortening of the DNA chain as well as changingthe distances between i-motif strands. These observations were confirmedby i-motif’s RMSD curves. The contact between the DNA fragmentand graphene sheet always results in a change of the i-motif spatialstructure. The analysis of fluctuations of i-motif atomic positionsshows that the adsorption on graphene does not affect strongly bondedsemiprotonated cytosine pairs. The fluctuations of 3′ thymineare large when it is adsorbed on GR-H and almost the same in othercases. When the DNA fragment is adsorbed on GR-COOH, a notable flexibilityof thymine and adenines appears. The adsorption of the i-motif onGR-NH2 is also distinguished fromthe others by the location of the adsorbed DNA fragment on the graphenesurface. For almost all simulation trajectories, the i-motif is adsorbedvery close to the amine-doped graphene edge. Moreover, only in thecase of the i-motif/GR-NH2 dimer, the adsorption influencesthe internal H-bonds formed between nucleobases. These hydrogen bondsbetween nucleotides inside the i-motif structure have a strong characterwith the dominant of strongly covalent interactions.The system i-motif/GR-NH2 dimer is the most stable among the considered systems.The biasedMD simulations confirmed the strongest i-motif adsorptionon the amine-doped graphene. The work necessary to drag it out fromthe surface is almost twice larger than in other cases. Dragging DNAfrom the graphene surface causes significant changes in its shape.Thus, one of the most important conclusions is that the i-motif/GR-NHThe observed features of investigated systems are very promisingand encourage further experimental and theoretical research.


Clear cell renal cell carcinoma (ccRCC) is a common aggressive malignant tumor of the urinary system. Given the heterogeneity of the tumor microenvironment, immunotherapy may not fully exert its role in the treatment of advanced patients. Long noncoding RNA (lncRNA) has been reported to be critically associated with the differentiation and maturation of tumor-infiltrating lymphocytes (TILs), which work against tumor cells. In this study, we identified 10 TIL-related lncRNAs  by Pearson correlation, univariate Cox regression, Lasso regression, and multivariate Cox regression based on The Cancer Genome Atlas (TCGA) database. A risk score model was established based on these lncRNAs. Next, a nomogram was constructed to predict the overall survival. By employing differentially expressed genes (DEGs) between groups with high and low risk scores, gene ontology (GO) enrichment analysis was performed to identify the major biological processes (BP) related to immune DEGs. We analyzed the mutation data of the groups and demonstrated that SETD2 and BAP1 had the highest mutation frequency in the high-risk group. The “CIBERSORT” R package was used to detect the abundance of TILs in the groups. The expression of lymphocyte markers was compared. We also determined the expression of two lncRNAs (AC084876.1 and AC026401.3) and their relationship with lymphocyte markers in the kidney tissue of ccRCC patients and showed that there was a positive correlation between AC084876.1 and FoxP3. Proliferation, migration, and invasion of AC084876.1-downregulated ccRCC cell lines were inhibited, and the expression of PD-L1 and TGF-β secretion decreased. To our knowledge, this is the first bioinformatics study to establish a prognostic model for ccRCC using TIL-related lncRNAs. These lncRNAs were associated with T-cell activities and may serve as biomarkers of disease prognosis. Renal cell carcinoma (RCC) is a common malignant tumor of the urinary system. Globally, 431,288 new cases  and 179,368 new deaths  occurred in 2020, second only to prostate cancer and bladder cancer ; however+ T cells  have a length of over 200 nucleotides and are dynamically expressed in the immune system and regulate the differentiation and function of immune cells . Previouin vitro experiments.With the maturation in the application of high-throughput sequencing technology, it has become possible to explore the expression levels of lncRNAs in ccRCC and their relationship with tumor-infiltrating lymphocytes; additionally, bioinformatics analysis can be applied to further explore the relevant mechanisms. In this study, we identified a signature consisting of 10 TIL-related lncRNAs to predict the prognosis of patients with ccRCC and explored the potential mechanism. Human renal tissues were used to detect the expression levels of related lncRNAs and immune markers. Finally, the biological functional effect of AC084876.1 on the ccRCC cell line was examined by This study was approved by the Ethics Committee of Guangzhou First People’s Hospital, School of Medicine, South China University of Technology. Informed consent forms were signed by all patients. In accordance with the ethical and legal standards, seven pairs of matched frozen samples of ccRCC and benign renal tissue adjacent to cancer from ccRCC patients were handled and made anonymous.2 incubator with RPMI-1640  medium containing 10% fetal bovine serum, streptomycin, and penicillin.A human ccRCC cell line 786-O was obtained from ATCC . The cells were cultured at 37°C in a 5% COhttps://cancergenome.nih.gov/) database. A total of 507 patients with follow-up information were identified and used for further analysis. The total RNA expression data were standardized through log2 transformation.The RNA-sequencing data and corresponding clinical information of patients with ccRCC were downloaded from the Cancer Genome Atlas  regression. Then, TIL-related lncRNAs identified by Lasso were included in a multivariate Cox model to establish a risk score. Finally, we identified 10 TIL-lncRNAs associated with the prognostic risk to construct a prognostic risk score. The risk score of KIRC patients was calculated as follows: Risk score = The correlation between lncRNAs and TIL-related genes  was calcBased on the TCGA-KIRC cohort, the expression of TIL-related lncRNAs was measured by ‘tinyarray’ R package. The Kaplan–Meier (K–M) survival curves of overall survival (OS) were used to evaluate the clinical prognostic value of TIL-related lncRNAs. Cytoscape software 3.7.2 was applied to visualize coexpression networks between TIL-related lncRNAs and TIL-related genes. Sankey diagram was used to assess the association between prognostic TIL-related lncRNAs, TIL-related genes, and risk types. The K–M survival curves were created to compare the overall survival of high- and low-risk groups according to predictive signatures. The receiver operating characteristic (ROC) curve from the “survivalROC” R package was used to assess the sensitivity and specificity of the signature. Univariate Cox regression and multivariate Cox regression were applied to assess the prognostic value of the signature.Based on age, clinical stage, risk score, and pathological grade, a nomogram for OS was developed with the R package “rms” to predict the 1-year, 3-year, and 5-year relapse-free survival probability. The concordance index (C-index) was used to evaluate the consistency between the nomogram-predicted results and the actual observed results. A calibration curve was used to show the difference between the predictions of the model and the real outcomes. ROC analysis was performed to evaluate the predictive ability of the risk score. The web-based OS probability calculators were built using packages “DynNom” and “shiny” in R software.P< 0.05. Then, gene ontology (GO) enrichment analysis was performed to find the major biological process (BP) related to immune DEGs. The visual GO enrichment maps of the annotation analysis results were obtained by R with the “ggplot2” and “GOplot” packages.Differentially expressed genes (DEGs) of mRNA between the high-risk and low-risk groups were identified using package limma in R, with thresholds of |log2 fold change (FC)| > 2 and adjusted Mutation data of the high-risk group and the low-risk group were analyzed and visualized using the “maftools” package. Mutation information for each gene in each sample was demonstrated by waterfall plots.http://cibersort.stanford.edu) was used to measure the abundance of tumor-infiltrating immune cells in a gene expression matrix by linear support vector regression . Real-time quantitative PCR (RT-qPCR) was carried out using the AceQ® qPCR SYBR Green Master Mix . The relative expression of lncRNA was calculated based on the internal reference β-actin. All experiments were carried out in three replicates. The primers applied are listed in Total RNA was extracted from ccRCC and corresponding benign renal tissue samples using NucleoZOL reagent . Total RNA was then reverse-transcribed into cDNA using HiScriptQuantitative analysis of protein expression in clinical tissues was performed using western blotting in accordance with the protocol of our previous study . The folHuman AC084876.1-specific siRNA and negative control siRNA were purchased from Tsingke Biotechnology Co., Ltd. . The siRNA sequences are shown in the Cell proliferation was measured by CCK-8  assay as previously described .Cell migration and invasion were measured by a wound healing assay and transwell assay in line with the protocol from our previous study .The assay was conducted using a human TGF-β ELISA kit . The cell culture medium was tested in accordance with the manufacturer’s instructions. The results were normalized using cell counts.t test or analysis of variance (ANOVA) was used to determine the statistical significance of quantitative data. P< 0.05 was regarded as statistically significant.All statistical analyses were performed in R software (version 4.03) and GraphPad Prism 8 . Continuous variables are shown as the means ± standard deviations. Student’s P = 0.019), LINC00460 , AC026401.3 , LINC00944 , LINC01615 , AL162586.1 , and AC084876.1  served as negative prognostic factors, while other three lncRNAs, i.e., LINC02027 , AC147651.1 , and AP000439.2  were favorable prognostic factors in ccRCC.This study was performed following the workflow shown in Next, correlation analysis was conducted. As shown in P< 2.2e-16), LINC00460 (P = 2.1e-06), AC147651.1 (P = 6.6e-07), AC026401.3 (P< 2.2e-16), LINC00944 (P< 2.2e-16), LINC01615 (P< 2.2e-16), AP000439.2 (P< 2.2e-16), AL162586.1 (P< 2.2e-16), and AC084876.1 (P< 2.2e-16) were significantly upregulated, while LINC02027 (P = 2.1e-06) was significantly downregulated in tumor tissue compared with normal tissue  + (−0.1762 × expression level of LINC02027) + (0.0487 × expression level of LINC00460) + (−0.0199 × expression level of AC147651.1) + (0.0604 × expression level of AC026401.3) + (0.2158 × expression level of LINC00944) + (0.0509 × expression level of LINC01615) + (−0.0134 × expression level of AP000439.2) + (0.2292 × expression level of AL162586.1) + (0.2125 × expression level of AC084876.1).Based on the median cutoff value, patients in the cohort were divided into the high-risk group (n = 253) and the low-risk group (n = 254) .For the clinical usability of the model, a dynamic nomogram was created for the prediction of OS probability in patients with ccRCC . No statistically significant results were found in other correlation analyses . The growth curve suggested that the knockdown of AC084876.1 inhibited the growth of the 786-O cell line (P< 0.01). The migration ability (P< 0.01) and invasion ability (P< 0.01) were attenuated in AC084876.1-downregulated 786-O cell lines.siRNAs were used to knock down the expression of AC084876.1 in the 786-O cell line. The transfection efficiency was measured by RT-qPCR, and we showed that si-1 and si-2 significantly interfered with the expression of AC084876.1 (P< 0.01) and decreased secreted TGF-β (P< 0.01).PD-L1 expressed on the surface of tumor cells and TGF-β secreted by tumor cells have been shown to regulate the activity and differentiation of tumor-infiltrating Tregs . To furtccRCC is the most common pathological subtype of renal carcinoma. Although at an early stage patients can benefit from surgical treatment, advanced patients have finite treatment options . Immunotin vitro. Thus, lncRNAs in the signature were tightly related to TILs and reliable to further investigate the potential role of TIL-related lncRNA signature in the progression of ccRCC.In the present study, we established a novel risk coefficient model based on TIL-related lncRNAs through Pearson correlation analysis, univariate Cox regression, Lasso regression, and multivariate Cox regression in 507 ccRCC patients from the TCGA-KIRC dataset. Finally, 10 TIL-related lncRNAs  were identified and included to construct the prognostic signature. Considering the results of survival analysis, Sankey diagram, and expression analysis of lncRNAs in the high-risk group, AC026401.3, LINC00944, and AC084876.1 were risk factors, whereas AP00439.2, AC14765.1, and LINC02027 were protective factors. Exploring the expression of biomarkers in experiments, we found that the expression trends of AC084876.1 and AC026401.3 in benign and malignant kidney tissues were consistent with the results of bioinformatics analysis, although the differences did not reach the threshold of statistical significance. A larger sample size is needed for further confirmation. The overall survival of ccRCC patients in the TCGA cohort with high expression of AC084876.1 was worse, and AC084876.1 was present as a risk factor in the prognostic model. The experimental validation indicated that the knockdown of AC084876.1 inhibited the growth, migration, and invasion ability of the ccRCC cell line The survival analysis indicated that the survival rate of patients in the low-risk group was significantly higher. Univariate Cox regression and multivariate Cox regression analysis showed that the risk score could be an independent predictor of overall survival in patients with ccRCC. In the nomogram model, risk score contributed the most, and the calibration curves of 1-, 3-, and 5-year survival prediction were close to the ideal value. Also, the AUC values of the 1-, 3-, and 5-year nomograms were greater than those of every single clinical predictor, indicating that the nomogram could be clinically helpful. To make the model applicable for clinical use, we constructed a dynamic nomogram for convenient and intuitive individual prognosis prediction based on the personal characteristics of ccRCC patients.Consistent with previous studies based on the immunotherapy effectiveness in ccRCC , 29, we GO-BP analysis of DEGs showed that the immune-related biological processes were mostly involved in T-cell activation, differentiation, proliferation, co-stimulation, and migration. By applying CIBERSORT, we found that the abundance of activated CD4+ memory T cells, CD8+ T cells, TFH, and Tregs was higher in the high-risk group. CD8+ T cells are the main executors of killing tumor cells in the immune system. However, high abundance of CD8+ T cells was not associated with a favorable prognosis in ccRCC , and it via increasing consumption of IL-2 and upregulating inhibitory immune checkpoints ; Guangdong Basic and Applied Basic Research Foundation 2017A030310100 (KG); Science and Technology Projects in Guangzhou 202201010726 (ZH); Science and Technology Development Fund of Macau SAR 0031/2021/A (WZ); Emergency Key Program of Guangzhou Laboratory EKPG21-04 (WZ); Guangdong Basic and Applied Basic Research Foundation 2020A1515110792, 2022A1515010342 (JY).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


A medium-chain triglyceride (MCT) oil microcapsule was prepared by spray drying. The effects of the wall-material parameters of wall-to-oil ratio (1:1 to 3:1) and type of wall material (gum arabic (GA), whey protein isolate (WPI), and octenyl succinic anhydride (OSA) starch) on the microcapsules were evaluated. The droplet size, size distribution, viscosity, zeta potential, and stability of the emulsions were measured. The spray-dried powder was characterized by its morphology, yield, encapsulation efficiency, and moisture content. The wall material influenced the characteristics of the emulsions and microcapsules. The formulation with a 2:1 wall-to-oil ratio and OSA starch/maltodextrin as the wall material resulted in a small droplet size (0.177 ± 0.002 µm) with high encapsulation efficiency (98.38 ± 0.01%). This formulation had good physical stability over three months under accelerated conditions. Thus, OSA starch/maltodextrin is an appropriate wall material for encapsulating MCT oil. The medium-chain triglyceride (MCT) oil is a type of dietary triglyceride that is synthetically produced from rich resources, such as palm kernel oil and coconut oil, by fractionation ,2. The pd-glucuronic acid, d-galactose, l-arabinose, and L-rhamnose. GA is one of the most commonly used wall materials owing to its excellent emulsifying properties and ability to form stable emulsions over a wide pH range ; De Brouckere mean diameter) and was calculated using the following equation: i is the number of particles of diameter di, and by:10, d50, d90 are the equivalent volume diameters at 10%, 50%, 90% cumulative volumes, respectively. A smaller span value indicates a narrower size distribution. Freshly prepared emulsions were characterized using a Mastersizer 2000 laser diffraction particle size analyzer . The refractive index of the sample and dispersant was 1.52 and 1.33, respectively. The droplet size was expressed as the mean volumetric size  equipped with a Z34 DIN sensor  operating at 500 rpm. The temperature was maintained at 30 °C using a thermostatic water bath. The zeta potentials of the emulsions were analyzed using a Zetasizer Nano-ZS photon correlation spectrophotometer . The samples were diluted 100 times with deionized water to avoid multiple scattering. All measurements were performed in triplicate.The emulsions were spray-dried using a model B-290 spray dryer  equipped with a standard nozzle . Feed flow rate and aspirator were set to 10% and 90%. Compressor air pressure for the spray flow was adjusted to 6 bar, while the inlet temperature was maintained at 150 °C. The outlet temperature was 95 ± 5 °C. The obtained spray-dried powder was stored at 2–8 °C until further analysis.Yield (%) was calculated as:Powder (0.5 g) was evenly spread over the aluminum pan of the model MA45 moisture analyzer  and heated at 105 °C. The drying was automatically stopped when a constant weight was obtained. Moisture content was presented in percentage (%). v/v) for 5 min. The mixture was separated by centrifugation  at 3000 rpm for 30 min. The clear upper part (organic phase) was collected and the lower aqueous phase was re-extracted using the same solvent mixture. The mixture was then filtered through a sodium sulfate column and the filtrate was evaporated under a fume hood. TO content was determined gravimetrically. For surface oil or free oil (FO), 15 mL of hexane was added to 2 g of the powder and mixed for 2 min. The mixture was then filtered through Whatman filter paper grade 1 and rinsed twice with 20 mL of hexane. The filtrate was then evaporated under a fume hood, and FO was determined by the mass difference. All measurements were performed in triplicate. EE was expressed as a percentage (%) and was determined using the following equation:The total oil (TO) content was determined as previously described  with slig.The morphology of the spray-dried microcapsules was examined by scanning electron microscopy (SEM) using a JSM-IT300 microscope . A small amount of powder was adhered to the stub using double-sided tape and coated with gold. The coated stub was examined under vacuum at a voltage of 10 kV and magnifications of 5000× One formulation was selected from the experimental design for the stability study. The encapsulated powder was packed in an aluminum foil bag, sealed, and stored at 4 °C (control) or 40 °C/75% relative humidity (RH). Samples were randomly collected and analyzed for %EE and moisture content at designated time points of 0, 15, 30, 60, and 90 days. p-value < 0.05 was statistically significant.All experiments were performed in triplicate and were statistically analyzed by analysis of variance (ANOVA), using Minitab software 19, followed by Tukey’s multiple comparisons test. A p < 0.05). As the wall-to-oil ratio decreased, droplet size increased. This could be explained by the insufficient amount of wall material at a low wall-to-oil ratio (1:1). High oil-loaded emulsions were less viscous than low oil-loaded ones. This could affect the stability of the emulsion and result in larger droplet sizes [The results of all emulsion formulations are summarized in et sizes . TherefoThe span values ranged from 0.186 to 0.807. A small span value indicates a homogeneously mixed emulsion. All the droplet sizes in this study showed monomodal distributions, except for F1 , as shown in p < 0.05). The viscosity of all emulsions gradually increased with increasing wall-to-oil ratios. However, the formulations with GA/MD showed very high viscosity when compared with WPI/MD or OSA starch/MD. These results agreed with previous findings that formulations using a combination of MD and GA showed higher emulsion viscosity when compared with the combination of MD and other polymers  [The viscosity results ranged from 11.75 to 96.36 mPa·s. The wall-to-oil ratio, type of wall material, and their interaction had significant effects on the emulsion viscosity  ,18,19. p < 0.05). In general, zeta potentials > |±30| mV can prevent emulsion droplets from aggregating and retain good stability [The zeta-potential values ranged from |−30.50| to |−54.40| mV. WPI/MD displayed the highest zeta-potential value, followed by GA/MD and OSA starch/MD. The parameters that statistically significantly affected the emulsion zeta potential from greater to lesser effect were the type of wall material, the interaction of wall-to-oil ratio and type of wall material, and the wall-to-oil ratio . The wall-to-oil ratio had a negative influence on the yield; as the wall-to-oil ratio increased, the yield decreased. Especially for GA/MD formulations, the higher yield of 85.07% (F1) was significantly decreased to 71.99% (F7) when the wall-to-oil ratio was increased from 1:1 to 3:1. Among all the formulations, the highest yield (88.57%) was achieved in F3 where the microcapsules were prepared using OSA starch/MD. F7, in which GA/MD was the wall material, produced the lowest yield (71.99%). In this scenario, the low viscosity of F3 (11.75 mPa·s) could be an asset for processing a higher yield than F7 (96.36 mPa·s), while both formulations were prepared with the same higher solid content (40%). This was similar to a previous report, as the high viscosity of the feed emulsion could prolong the drying time, resulting in a sticky powder that could affect the yield and increase loss [The yield ranged from 71.99% to 88.57%, as shown in ase loss . p < 0.05). As the wall-to-oil ratio increased, the moisture content of the microcapsules increased, but there was no statistically significant difference between the ratios of 2:1 and 3:1. Among the wall materials, the microcapsules prepared using OSA starch/MD showed a statistically higher moisture content than those prepared using WPI/MD and GA/MD. This may be due to the influence of the molecular size of the wall material, which may hinder the diffusion rate of particle crust formation [The wall-to-oil ratio and the type of wall material had a significant effect on the moisture content (ormation . A previormation . The moiormation . Howeverormation .p < 0.05). The %EE decreased with decreasing wall-to-oil ratio. This could be due to the insufficient amount of wall material to cover the formed droplets, which could increase the chance of droplet coalescence, especially with a low wall-to-oil ratio (1:1). This result agreed with previous findings that %EE was inversely affected by oil load [w/w) has been regarded as a suitable ratio, because a ratio below 2 could enhance surface oil formation, whereas a ratio above 4 could result in low loading capacity [The %EE results are shown in oil load ,25,26. Fcapacity . Since tThe formulation is intended for storage in a refrigerator due to the instability of MCT oil and WPI . TherefoMCT microcapsules were successfully prepared by spray drying. SEM images showed the absence of surface cracks. The wall-to-oil ratio, type of wall material, and their interaction could affect the characteristics of the emulsion and microcapsule. An exception was the moisture content, where the interaction of wall-to-oil ratio and type of wall material was not found. A 1:1 wall-to-oil ratio for GA/MD or OSA starch/MD was not suitable for encapsulating MCT oil because some oil droplets were found on the surface of the emulsions. Concerning wall material, the OSA starch/MD formulations yielded a higher %EE than those of the WPI/MD and GA/MD formulations. The F6 formulation, in which OSA starch/MD was used as the wall material at a 2:1 wall-to-oil ratio, resulted in a small droplet size and a low-viscosity emulsion. The spray-dried powder had a high EE, acceptable moisture content, and stability during the stability study. In conclusion, OSA starch/MD is an appropriate wall material for encapsulating MCT oil by spray drying.


However, state-of-the-art approaches treat variant calling from trios as three independent tasks, which limits their calling accuracy for Nanopore long-read sequencing data. For better trio variant calling, we introduce Clair3-Trio, the first variant caller tailored for family trio data from Nanopore long-reads. Clair3-Trio employs a Trio-to-Trio deep neural network model, which allows it to input the trio sequencing information and output all of the trio’s predicted variants within a single model to improve variant calling. We also present MCVLoss, a novel loss function tailor-made for variant calling in trios, leveraging the explicit encoding of the Mendelian inheritance. Clair3-Trio showed comprehensive improvement in experiments. It predicted far fewer Mendelian inheritance violation variations than current state-of-the-art methods. We also demonstrated that our Trio-to-Trio model is more accurate than competing architectures. Clair3-Trio is accessible as a free, open-source project at  Accurate identification of genetic variants in family trios in the human genome is an important task in genomics, which provides insight into precision medicine and phenotype understanding . The humSeveral state-of-the-art deep learning-based methods are available for calling small variants from Oxford Nanopore Technologies (ONT) data. They are based on two main designs: pileup and full-alignment. Clairvoyante , Clair  and NanoFor calling varaints with genetic information shared in family trios, two pilot studies based on DeepVariant  have beeGenerally, two research gaps remain for calling variants from trios for ONT data: (i) how to train the model to learn from the information about both individuals and that preserved in family trios and (ii) how to train the model to predict the following Mendelian inheritance, a basic feature in family trios. Unfortunately, these two questions have never been studied in the ONT data and remain unsolved in the community.https://github.com/HKU-BAL/Clair3-Trio.To fill the two main research gaps and improve variant calling from trios’ ONT data, we propose a new model: Clair3-Trio. Clair3-Trio is the first variant caller tailored for family trios ONT data with a Trio-to-Trio deep neural network model design that allows it to input the trio’s sequencing information and output all of the trio’s predicted variants. Using the Trio-to-Trio model, Clair3-Trio can efficiently call variants based on individual and family trio information. We also designed a loss function, MCVLoss (Mendelian Inheritance Constraint Violation Loss), to make the model explicitly encode the priors of Mendelian inheritance in trios to improve its variant calling (described in the Methods section). Based on our experiment on the Genome in a Bottle (GIAB) HG002 trio data , Clair3-Clair3-Trio consists of two main modules : (i) datFor data preprocessing, first, we use the Clair3 pileup model to efficiently find all genetic variants that can be easily predicted with high confidence, and then, we use WhatsHap to obtain all phase variants and haptag reads, based on the called heterozygous single nucleotide polymorphisms (SNP) to get phased alignments for the Clair3-Trio model. With all individuals’ haptaged alignments available, we use a simple heuristic approach to identify candidate positions that might have any genetic variants in the family, as follows: (i) the Clair3 pileup model grasps all positions with supporting alternative allele frequency exceeding 0.08 and outputs all individual variant and non-variant calling with confidence scores [The Clair3-Trio model is a Trio-to-Trio model that can input all alignments from the family trio and output all variants from the same trio. The inputs for the Clair3-Trio model are generated by merging phased full alignments from trios. For each individual, the full-alignment information is converted into eight different feature channels, as previously discussed for Clair3 . For eacThe neural network of the Clair3-Trio model consists of multiple layers: convolutional layers (Conv), residual convolutional layers (ResBlock), pyramid pooling layers and dense layers . Clair3-http://bib.oxfordjournals.org/).To train a Clair3-Trio model in family trio data, we applied (i) a label cleaning module (Representation Unification) to clean the training data, and (ii) a trio data filtering module (MCV filtering) to further filter Mendelian violation sites in the training data. The two modules were established based on experiments. We use the Representation Unification module from Clair3 to unify the true variants label with the alignment information in the training data. The Representation Unification model may include Mendelian conflict in the unification process. We added MCV filtering to discard a few candidate sites (0.05% of candidate sites) in training data that violated Mendelian inheritance constraints. After cleaning the data, we performed random downsampling to make the model increase its generalization at different levels of data coverage. We downsampled the data into a range of coverage of 10×, 30×, 60× and 80× for all samples, kept the child data at high coverage and downsampled only the parent samples for low coverage. After downsampling, we kept 30% of the data of each coverage combination to balance speed and performance, leading to 33 353 000 candidates (from the GIAB HG002 family) in our training dataset. With the training dataset available, Clair3-Trio was trained in a two-step procedure. First, we trained an initial model of Clair3-Trio via the focus loss function, and then, we fine-tuned the initial Clair3-Trio model with the addition of multiple task MCVLoss function. We also tried other training techniques, but they failed to improve Clair3-Trio. This is elaborated in Supplementary Notes (see Supplementary Data available online at Our approach differs from Clair3 mainly in the following ways:(i) Targeted for best accuracy, Clair3-Trio can call variants in all potential variant sites in a family, while Clair3 calls them in individuals. Clair3-Trio has much more relaxed candidate selection criteria for variant candidate selection than Clair3. Claire3-Trio has 100% of variants and 20% of reference sites, compared with 30 and 10%, respectively, in Clair3, so Clair3-Trio ends up with 2.2 times more candidates. On the other hand, the variant candidates in the Clair3-Trio model are the union of all trio members, resulting in 1.9 times more candidates than individual variants. Typically, Clair3-Trio calls 4.2 times more candidates, on average, than the default Clair3 for each sample.(ii) Clair3-Trio is a Trio-to-Trio model, which uses all data in the trio to predict all of the trio members’ variants directly and consistently, while Clair3 is a powerful individual variant caller, which can be treated as a One-to-One model. More information about the Trio-to-Trio and the One-to-One model is provided in (iii) The Clair3-Trio model uses the MCVLoss function for fine-tuning, adding penalties to the trio’s variant predictions that violate the Mendelian constraints, giving Clair3-Trio a comprehensive understanding of the family trio’s variant calling.With the Trio-to-Trio model’s architecture and MCVLoss function, our model is well-tailored for calling variants in a family, resulting in a substantial improvement in all the benchmark experiments see  section The Trio-to-Trio model can predict the trio’s variants with trio’s information, but how to explicitly add the Mendelian inheritance information to the model remains an open question. In the following subsections, we discuss the MCVLoss function, which is designed to control the Mendelian inheritance violation rate in the model. We briefly describe the original loss function in Clair3 and then introduce the MCVLoss function.First, we detail the original loss function for an individual, inherited from Clair3, to better illustrate the basic components in the Clair3-Trio loss function. The output of Clair3 includes four variant tasks—genotype, zygosity and two INDEL length tasks—as previously described in Clair3 . The mosr3 paper .We extended the model output in Clair3 from the individual to compute trio genotypes in Clair3-Trio. The probability of the trio members is represented asFor each individual’s probability, we simply have the property thatCombining formulas  and 5)  5) for tMCVLoss is based on the idea of penalizing the trio genotype that violates the Mendelian inheritance. For each trio genotype, we define a parameter  default . CombiniBased on formulas –8), we , we 8), With the MCVLoss available, the final Clair3-trio loss function isIn this manner, MCVLoss introduced the Mendelian inheritance prior to model training. The detailed results of using MCVLoss are presented in de novo variants in the model’s prediction, where the de novo variants [de novo variants are constrained in the confidence region, while the number of the Mendelian violations is computed in all sites.We use Precision, Recall and F1-score metrics to evaluate the family trio variant-calling performance in different configurations. The Precision, Recall and F1-score are computed via hap.py (v0.3.12) . We compvariants  are defide novo variants was constrained in the trio’s overlapped high-confident bed regions.We conducted our experiments on the dataset collected in the Genome in a Bottle (GIAB)  Ashkenazhttp://bib.oxfordjournals.org/) and http://bib.oxfordjournals.org/) (‘90x’ means ‘up to 90x’ in both figures and tables). For all variants, we observed that Clair3-Trio had a better performance in the F1-score than Clair3 and PEPPER. The performance gain was especially profound in the lower coverage data. Clair3-Trio achieved an F1-score of 92.85 and 92.12% at 10× data from HG002 (child) and HG003 (parent), compared with 82.78 and 86.77% in Clair3 and 54.77 and 65.70% in PEPPER, respectively. More details are provided in http://bib.oxfordjournals.org/).We compared the Clair3-Trio variant-calling performance against Clair3 and PEPPER at different coverage in individuals from the GIAB trio. The overall benchmark results are shown in http://bib.oxfordjournals.org/). We found that Clair3-Trio had higher performance in both SNP and INDEL than Clair3 and PEPPER. For SNP, Clair3-Trio performed better than Clair3 and PEPPER, especially below 40× coverage. For INDEL, Clair3-Trio showed consistently better results than Clair3 and PEPPER. For INDEL, Clair3-Trio achieved an F1-score of 78.07 and 77.40% at 60× HG002 (child) and HG003 (parent) data, respectively. This is much higher than 72.45 and 75.02% in Clair3 and 66.94 and 68.91% in PEPPER. These results verify the effectiveness of the Clair3-Trio model.The SNP and INDEL performance breakdowns are shown in , see Supplementary Data available online at http://bib.oxfordjournals.org/). The rationale is that for calling variants, the family trio provided more information about the child, which shares two haplotypes with parents, while each parent shares only one haplotype with the child.Comparing performance gain among members of the family trio, we found that the performance gain in the child (HG002) was much more profound than that in the parents (HG003 and HG004). For INDEL, Clair3-Trio achieved a +5.62% increment in the F1-score in the child compared with Clair3 at 60×, while the improvement dropped to +2.38% in the parents , at 10× coverage, there were 7072 Mendelian violations called from Clair3-Trio, while the number were 48 345 and 131 509 in Clair3 and PEPPER, respectively. At 60×, in contrast, there were 8429 Mendelian violations called from Clair3-Trio, and 30 725 and 20 559 in Clair3 and PEPPER, respectively. Regarding the number of Mendelian violations found in Clair3-Trio at 60×, 70.3 and 29.7% of the calls were SNP and INDEL, respectively, indicating a large proportion of SNP and INDEL Mendelian violations recognized by Clair3-Trio. In contrast, for the number of de novo variants, Clair3-Trio has fewer false-positive (FP) de novo variants than other tools had. Clair3-Trio had 197 FPs at 60× data compared with 458 and 455 in Clair3 and PEPPER, respectively. However, Clair3-Trio found slightly fewer true-positive (TP) de novo variants, 33, compared with 35 in both Clair3 and PEPPER. We gathered all false-negative de novo variant cases of Clair3-Trio in http://bib.oxfordjournals.org/) and present their alignment visualization in http://bib.oxfordjournals.org/). More discussion about the Mendelian violations and de novo variant calling for Clair3-Trio are presented in Comprehensively evaluating variants across all family members using metrics such as the number of Mendelian violations is important when calling variants in a family trio. In Mendelian inheritance violations, Clair3-Trio showed an order of magnitude fewer violations than Clair3 and PEPPER at 10× to 30× coverage. As shown in http://bib.oxfordjournals.org/). Clair3-Trio showed consistently better results than Clair3 and PEPPER in the HG005 trio. Clair3-Trio achieved an F1-score of 98.16 and 97.58% at 40× data from HG005 (child) and HG006 (parent), compared with 97.60 and 97.39% in Clair3 and 97.02 and 96.81% in PEPPER, respectively.We also benchmarked Clair3-Trio at the Chinese trio (HG005 trio) from GIAB. We obtained the ONT sequencing data  from HPRC  and the http://bib.oxfordjournals.org/).After the initial release of Clair3-Trio, both Clair3 and PEPPER released their new version with models supporting Guppy5 data. We trained a new Clair3-Trio model with Guppy5 data and benchmarked the new model with Clair3 (v0.1-r11) and PEPPER (r0.8)’s Guppy5 models. We found that the performance trend of Clair3-Trio for Guppy5 remains the same as in Guppy 4. Results are available in http://bib.oxfordjournals.org/), http://bib.oxfordjournals.org/) and http://bib.oxfordjournals.org/). For the child sample, the performance of Clair3-Trio is similar to that of Clair3 when the parent has very low coverage (10×) overall, indicating that 20× or more for parents is required for trio calling to improve the variant calling for the child. When the parents have half the child’s coverage , Clair3-Trio achieved an overall F1-score of 96.92%, compared with 96.50 and 95.98% for Clair3 and PEPPER, respectively. Separating the results from SNP and INDEL, we found that Clair3-Trio outperformed the other tools when parents had coverage higher than 10× for SNP calling and coverage higher than 30× for INDEL calling. Furthermore, in Clair3-Trio, there was a large improvement in the performance of low-coverage parent data when higher coverage for the child was provided . We found that Clair3-Trio remains to have a higher performance, with 93.69% of F1-score in 10× in the child, than 82.78% in Clair3 and 54.77% in PEPPER in this scenario.We also tested Clair3-Trio in a scenario where only the child has lower coverage (with 10× for the child and 30× for parents). The scenario with higher coverage for parents is easier for trio calling compared with lower or equal coverage for parents in trio data. The results are available in The improvement of Clair3-Trio on the trio data makes it useful for population genome projects in which better variant calling performance is expected for both parents and children.We first categorized different methods based on their input and output information to generalize different methods for variant calling from family child–mother–father trio data. The One-to-One model inputs single sample information and outputs single sample variants. Clair3, PEPPER and Medaka are typical One-to-One models. The Trio-to-One model inputs data from three samples into the model and outputs single sample variants. For example, DeepTrio, which works with Illumina and PacBio HiFi data, is a typical Trio-to-One model. Finally, the Trio-to-Trio model inputs data from three samples into the model and outputs the three samples’ variants simultaneously. In Clair3-Trio, we built the first Trio-to-Trio model.To compare the performance of different architectures, we ablated the input and output tensors of Clair3-Trio models accordingly to test as three architectures: One-to-One, Trio-to-One and Trio-to-Trio models. The One-to-One model has single sample input and predicts single sample variants, as in Clair3 and PEPPER. The Trio-to-One model has information of three samples in its input but predicts single sample variants in its model, as in DeepTrio. The Trio-to-Trio model is a native version of Clair3-Trio, which has three samples input and three samples output, but with deactivated MCVLoss and fine-tuning. We trained a single model for all architectures on chromosome 1 64× data from the GIAB HG002 trio and tested the performance on chromosome 20. For the Trio-to-One model, which is sample order specific, we trained two models separately to make predictions: a child model and a parent model. The benchmark results for the child as well as the number of Mendelian violations are in We found that including trios information in the model efficiently improves the variant calling performance overall, especially in terms of Mendelian inheritance violations . SwitchiTo further explore the best architecture for the Trio-to-Trio model, we also evaluated the effect of using different model shapes. With three inputs and three outputs available, we developed multiple candidates for model shape, as illustrated in The MCVLoss (Mendelian Inheritance Constraint Violation Loss) function is designed to improve variant calling in trios by leveraging the explicit encoding of the priors of the Mendelian inheritance in trios. We found that MCVLoss can effectively reduce Mendelian violation prediction in variant calling. However, the prediction is better accompanied with fine-tuning techniques, in which we train a Clair3-Trio model in two steps: (i) training Clair3-Trio without MCVLoss with the default learning rate (1e−3 in our setting) and (ii) fine-tuning the trained Clair3-Trio model with MCVLoss with a lower learning rate (1e−5 in our setting). When using the fine-tuning technique alone, the F1-score from HG002, HG003 and HG004 had a performance boost of +0.2% . We got We also evaluated the effect of using a different  MCVLoss . The \dohttp://bib.oxfordjournals.org/).We inherited the highly optimized modules from Clair3 and created a Clair3-Trio workflow  with parhttp://bib.oxfordjournals.org/), two genotypes 0/1 and 0/2 in the child had an equal number of read supports in 10× data. Clair3 and PEPPER failed to call the variant base only on the child’s data. Clair3-Trio called the child genotype as 0/1 correctly with information from the parents at the same site.Clair3-Trio outperformed single sample callers especially at lower coverages, making sequencing a family trio at a relatively lower coverage more favorable than sequencing only the child to a high coverage. As an example, in We found that most of the Mendelian violation cases from Clair3-Trio (68.6%) for parent-1, parent-2 and child, respectively, are: , ,  and  . All thede novo variants predicted than Clair3 and PEPPER. The drop in TP of de novo variants is expected, as Clair3-Trio is designed to predict variants by leveraging information from family trios that favor having fewer Mendelian violations in their prediction. For detecting de novo variants that do not follow Mendelian inheritance, One-to-One model-based methods such as Clair3 and PEPPER can be used to supplement Clair3-Trio.Clair3-Trio has high performance overall, but it has fewer There are some challenges and future works needed regarding trio variant calling from ONT data. Experiments show that Clair3-Trio’s improvement over state-of-the-art methods is profound when the trio data have similar coverage among family members, but it only marginally improves with calling variants from different data coverage (such as child coverage of 60× and parent coverage of 10×). These results leave room for further improvement in trio calling in diverse coverage applications. The current model is trained with multiple coverage down-sampled from the full coverage, but only with the coverage of the child kept equal to or larger than that of the parents, and not the cartesian product of the down-sampled coverage of the three samples. This is a practical decision to reduce the amount of training data and since the coverage of the child in a trio is usually higher than that of the parents. However, this may also challenge Clair3-Trio when the coverage of parents exceeds that of the child. An improved training scheme is expected to handle the large amount of training data when all coverage combinations are used. On the other hand, there is a research gap in applying variant calling in the human sex chromosome region. The current training and testing was constrained to the autosome region, which assumes that the variants are diplotypes and inherited from one of the parents. However, on the sex chromosome, the assumption is unheld when calling variants in the child’s Y chromosome, which is a haplotype and is obtained only from the father’s side. Currently, there are no tools available for calling variants in the sex chromosome region with the family information from ONT data. We need a new design for calling variants from the sex chromosome region to fill this research gap. In the future, we would like to design a heuristic approach to solve the question: if the child is female, use Clair3-Trio directly at the sex chromosome; if the child is male, use Clair3-Trio to call variants in the pseudoautosomal regions (PAR1 and PAR2) of the sex chromosome and build a tailored haplotype model to call variants in the remaining regions.In conclusion, we introduced Clair3-Trio, a high-performance Nanopore long-read variant caller in family trios with a Trio-to-Trio deep neural network. Clair3-Trio is the first family trio variant caller tailored for Nanopore long-read data with a Trio-to-Trio deep neural network model and MCVLoss. In our experiments, Clair3-Trio outperformed current state-of-the-art methods on trio variant calling in terms of F1-score and the number of Mendelian inheritance violations in all three samples from a trio. We also demonstrated that the architecture of the Trio-to-Trio model is much more accurate than the One-to-One and Trio-to-One model. The source code and the results of this study are publicly available on GitHub.R.L. conceived the study. J.S. and R.L. designed the algorithms, implemented Clair3-Trio and wrote the paper. All authors evaluated the results and revised the manuscript.Developed a Trio-to-Trio model to predict trio variants in ONT data.Introduced a novel loss function, MCVLoss, to model Mendelian inheritance in trio data.Demonstrated that the Clair3-Trio model trained on GIAB data improves variant calling in trio data.Demonstrated that Trio-to-Trio models can efficiently decrease Mendelian inheritance violations compared with One-to-One and Trio-to-One models.supplementary-bib_bbac301Click here for additional data file.


IntroductionThe exact mechanisms of obesity-related kidney disease (ORKD) are not fully known. Heat shock proteins (HSPs) may play a role in ORKD mechanisms because of their role in cell apoptosis, cytoprotection, and inflammatory processes. We aimed to determine the role of circulating serum HSP-60 and HSP-70 levels as a biomarker for ORKD.Materials and methodsThis study included 40 ORKD patients, 40 obese age-matched and sex-matched controls with similar body mass index (BMI), and 40 healthy controls. Their serum biochemical and hemogram parameters as well as HSP-60 and HSP-70 levels were evaluated and compared. Their neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein levels were assessed to define inflammation. ResultsThe patients had significantly higher HSP-60 levels than the obese and healthy controls . The results revealed that the 24-hour urinary protein levels had a positive correlation (r= 0.544), whereas the glomerular filtration rate had a negative correlation (r = 0.38) with the serum HSP-60 level. According to the regression analysis performed on the HSP-60 and 24-hour urinary protein excretion levels, an increase in the HSP-60 level significantly increased the 24-hour urinary protein excretion rate . The HSP-60 levels were correlated with inflammatory markersConclusionThe serum HSP-60 levels increased in patients with ORKD. This increase was correlated with 24-hour urinary protein excretion. Increased circulating levels of HSP-60 may play a role in the initiation and/or progression of renal damage and inflammation. HSP-60 is a potential biomarker for ORKD. However, additional information and studies are required to further elucidate this finding. Obesity is a major health problem, and its prevalence is increasing along with the global obesity epidemic, which is affected by genetic, physiological, and sociocultural factors. Obesity is a critical, correctable, and independent risk factor for the development of chronic kidney disease (CKD). A positive correlation exists between increased body mass index (BMI) and proteinuria even before CKD progression . FurtherHeat shock protein (HSP) molecules have different roles depending on their intracellular or extracellular expression. They are mainly involved in the degradation and clearance of misfolded proteins as well as in chaperone-mediated autophagy, protein quality control mechanisms, cell signaling, stress response formation, and kidney self-defense systems . In rat Hypertrophy, hyperplasia, and inflammation in adipocytes cause many changes in adipose tissue structures and adipokine secretion. Increased leptin levels in obese patients activate the sympathetic system and cause sodium retention and hypertension. Furthermore, increased leptin levels accelerate kidney disease progression by increasing endothelial dysfunction. The release of proinflammatory cytokines such as interleukin (IL)-6 and tumor necrosis factor-alpha (TNF-α) in the adipose tissue of obese patients causes inflammation and oxidative stress ,10. A deAlthough it is known that proinflammatory mediators are secreted from adipocytes, the physiological signals that trigger their release have not been fully determined. HSP-60 is an important inductor in the release of proinflammatory cytokines from the adipose tissue . In addiHSP-60 has recently been identified as a new molecular link in obesity-related inflammation and insulin resistance . The rolStudy designThis study was designed as a cross-sectional observational study. It included 120 participants who were consecutively admitted to our internal medicine and nephrology outpatient clinics. This study comprised 40 obese CKD patients who did not have diabetes and 40 obese controls who were of similar age, sex, mean BMI, and normal renal function, and 40 healthy controls. We define ORKD as the presence of proteinuria above 300 mg/day or GFR below 60 mL/min, as described in the literature .The inclusion criteria for the study were individuals between 18 and 60 years old who had a BMI of over 30 without diabetes mellitus. The weight (kg)/height (m2) formula was used to calculate the BMI. The main exclusion criteria were the presence of comorbidities  that may cause chronic renal disease. Demographic data and patient history were recorded.Ethical consideration An informed consent form was signed by the participants in this study. This study was approved by the Ethics Committee of the University of Health Sciences Bozyaka Training and Research Hospital, Turkey, on June 30, 2020, with decision number 248.Laboratory analysisThe hemogram and biochemical parameters of all the participants, including urea, creatinine, uric acid, and C-reactive proteins (CRPs), were studied. Detailed analyses of the biochemical parameters and a complete blood count (CBC) were performed on all the participants. The neutrophil count was divided by the lymphocyte count, and the neutrophil-to-lymphocyte ratio (NLR) was calculated. The e-GFR was calculated using the Modification of Diet in Renal Disease formula. The biochemical parameters were analyzed using an Olympus AU2700 Chemistry analyzer . Urine samples were collected over 24 hours for proteinuria analysis. Each participant’s blood was collected in a clot-activating tube with a gel separator, centrifuged at 1500 × g for 10 min, and then stored at -80 °C until HSP analysis. The serum HSP-60 and HSP-70 levels were determined using a ready kit and the sandwich enzyme-linked immunosorbent assay (ELISA) method (SinoGeneClon Biotech Co. Ltd). Analyses were made based on the instructions written in the kit prospectuses. An ELISA device  with a 450 nm wavelength was used for spectrophotometric measurements. The serum HSP-60 and HSP-70 concentrations were calculated by creating an absorbance concentration graph. The results are expressed as ng/ml. Statistical methodSPSS Statistics v. 18.0  was used for statistical analysis. The data is presented as mean ± SD. In this study, a one-way analysis of variance (ANOVA) was performed on the three groups. A homogeneity of variance test was performed for all the parameters. Bonferroni and Tamhane’s tests were used for post hoc analysis. Since the three groups were compared, the new p-value was determined as 0.017 by applying Bonferroni correction at the significance level. The relationship between HSP-60, HSP-70, and other demographic and laboratory data was evaluated. Our study included 40  ORKD patients, 40  obese patients with normal renal function, and 40  healthy volunteers. There was no statistically significant difference in terms of age and sex p=1). Table . Table 1The serum HSP-60, serum HSP-70, BMI, 24-h urinary protein excretion, urea, fasting blood glucose, creatinine, e-GFR, CRP, and NLR levels of the three groups were compared. As expected, there were no differences in the blood glucose levels because the study was conducted on non-diabetic subjects (p=1). The obese patients and obese control group had a statistically significantly higher BMI than the healthy controls (p<0.001). The obese patients had statistically significantly higher levels of GFR, urea, creatinine, 24-h urinary protein excretion, CRP, and NLR than those in the control groups. The HSP-60 levels were relatively high in the patient group (p<0.001), but the HSP-70 levels did not exhibit any significance. Table We performed post hoc analyses to determine the biochemical and hemogram differences between the patient and control groups. Tamhane’s post hoc multiple comparison test revealed a statistically significant difference in HSP-60 levels between the patient and two control groups Table . This diA regression analysis was performed to compare the HSP-60 and 24-hour urinary protein excretion levels. This analysis revealed that the 24-hour urinary protein excretion level significantly increased as the HSP-60 levels increased . We performed an analysis to determine the correlation between the HSP-60 24-hour urinary protein excretion and e-GFR levels. There was a strong positive correlation between the HSP-60 and urinary protein excretion levels  and CRP levels . In addition, there was a relatively positive correlation between HSP-60 levels and NlLR . There was a strong negative correlation between the e-GFR and HSP-60 levels .In this study, we investigated the role of serum HSP-60 and HSP-70 levels in nondiabetic ORKD patients. We included both obese and healthy controls to assess the significance of the study. The patient group had significantly higher serum HSP-60 levels than the control group. This increase was more significant between the healthy control and patient groups (p<0.001). Conversely, there was no significant difference between the HSP-70 levels of the three groups.Previous studies have investigated the relationship between HSPs and obesity and metabolic syndrome. It has been stated that intracellular HSP-70 and HSP-27 expression may play an important role in the development of metabolic syndrome in obese individuals . The litHSP molecules are one of the basic body defense mechanisms that are active during stress. However, there are limited studies on HSP molecules in renal diseases. It has been demonstrated that HSP-70 levels are elevated in chronic glomerulonephritis and that HSP-60 mRNA and protein levels are elevated in proximal tubular cells after heat stress ,20. A prHauffe et al. demonstrated that decreased HSP-60 levels in mice result in beneficial changes in adipose tissue morphology, body weight, and insulin resistance . We cameIn this study, the obese patient group had statistically significantly high CRP and NLR levels. This may be due to the high inflammatory state caused by obesity and CKD. In recent years, researchers have discussed the role of inflammation in obesity, revealing that obesity is a subclinical inflammatory disease. IL-6 and IL-8, transforming growth factor-β, fibroblast growth factor, epidermal growth factor, TNF-α, and CRP are some of the factors that are secreted from the adipose tissue . Habich The limitations of the study were that the study was cross-sectional and we cannot be completely sure if high HSP-60 levels in obesity caused CKD, or if renal failure was diagnosed based on laboratory findings (GFR and 24-hour urinary protein excretion levels), and renal biopsies were not performed on the patients because there was no indication. We believe that our study can be supported with tissue biopsies from obese renal failure animal models that have had their HSP molecules stained.This is the first study to focus on HSP molecules and inflammation in nondiabetic obese renal failure patients. This study demonstrated that HSP-60 and inflammatory markers are statistically significantly high in ORKD patients, whereas HSP-70 exhibits no difference. There was a strong positive correlation between HSP-60 and urinary protein excretion. These findings suggest that HSP-60 plays a role in the initiation and/or progression of renal damage and that it is a chaperone that triggers inflammation in obese individuals. The findings of this study will benefit from large multicenter studies and animal models so that their renal biopsies can be evaluated.


In the treatment study, there was no significant difference between the ivermectin and placebo group for any Day 14 treatment outcome: proportion with oxygen desaturation , change in WHO score from baseline , and symptom resolution . The ivermectin group had a significantly higher proportion of transient blurred vision . Our study failed to demonstrate the efficacy of a 3-day once daily of ivermectin for the prevention and treatment of COVID-19. The given regimen of ivermectin should not be used for either prevention or treatment of COVID-19 in populations with a high rate of COVID-19 vaccination. The safety and efficacy of ivermectin for the prevention and treatment of COVID-19 are still controversial topics. From August to November 2021, we conducted a double-blinded, randomized controlled trial at Siriraj Hospital, Thailand. Eligible participants were adults ≥ 18 years with suspected COVID-19 who underwent a SARS-CoV-2 RT-PCR test. After enrollment, the participants were randomized to receive either ivermectin (400–600 µg/kg/d) or placebo once daily for 3 days. Among 983 participants, 536 (54.5%) with a negative RT-PCR result were enrolled in the prevention study, and 447 (45.5%) with a positive RT-PCR result were enrolled in the treatment study. In the prevention study, the incidence of COVID-19 on Day 14 was similar between the ivermectin and the placebo group (4.7% vs. 5.2%;  The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a major health threat, with almost 440 million confirmed cases and six million deaths globally as of 1 March 2022 .50) of 2 µM . The proportions were also similar in the mITT analysis , with a difference of −0.41%   and the placebo (plus favipiravir) for all outcomes, including the proportion of participants with oxygen desaturation; the change in WHO progression score from baseline; the absence of symptoms at Day 3, Day 7, and Day 14; 14-day hospitalization rate; and 28-day mortality . Most syp = 0.337). However, there were more ocular AEs reported in the ivermectin group . These were mainly blurred vision while taking ivermectin, but this spontaneously resolved after completing the medication. An analysis of AEs by the study cohort found that ocular problems were more prevalent in the ivermectin group than the placebo group in the treatment cohort . Headache was reported more often in the placebo group  (The incidences of AEs in participants in both groups are shown in = 0.027) . No seriTo the best of our knowledge, this was the first large, double-blinded, randomized controlled trial to determine the safety and efficacy of ivermectin for both the treatment and prevention of COVID-19 in the same outpatient setting. A high dose of ivermectin (400–600 µg/kg/d) for 3 days did not show a significant benefit for the prevention of SARS-CoV-2 infection. Similarly, early treatment with the same dose and duration of ivermectin did not reduce disease progression or hospitalization in patients with mild-to-moderate COVID-19 compared with the placebo group. No serious AEs were reported in this study. However, eye-related symptoms, particularly blurred vision, occurred more frequently in the ivermectin group, especially in those who concomitantly received favipiravir.In this study, there was a low rate of acquiring SARS-CoV-2 infection (5%) even though the study was conducted among people with high-risk exposure. Ivermectin did not show a benefit for preventing SARS-CoV-2 infection, which is in contrast with some previous studies. A recent open-labeled randomized study evaluated 303 asymptomatic household contacts in Egypt found that the proportion of clinically diagnosed SARS-CoV-2 infections was 7.4% in the ivermectin group and 58.5% in the control group . AnotherOur study demonstrated that early treatment with ivermectin did not reduce COVID-19 disease progression or the hospitalization rate and did not increase symptom resolution.Several randomized controlled trials on the efficacy of ivermectin for treating COVID-19 have shown conflicting results in terms of virological and clinical outcomes ,16. HoweThe lack of observed differences in clinical outcomes between ivermectin and placebo in our treatment study should not be related with using favipiravir as standard of care. From recent systematic reviews, favipiravir did not show a significant benefit on the viral clearance and mortality . In addi50 against SARS-CoV-2 was found to be 2 µM, which is >35 times higher than the maximal plasma concentration after oral ivermectin administration at the approved dose of 200 µg/kg [An adequate and safe dose of ivermectin for treating COVID-19 has not been clearly established. Ivermectin’s IC00 µg/kg . The pre00 µg/kg . However00 µg/kg . A recen00 µg/kg . Our stuThe significantly higher rate of transient blurred vision in the ivermectin group has been documented. The previous malaria study reported a significant high rate of transient visual disturbance: 8% among those who receive a moderate dose of ivermectin (300 µg/kg for 3 days) and 22% among those who received a high dose of ivermectin (600 µg/kg ivermectin for 3 days) [Our study has some limitations. In the ivermectin prevention study, we used self-conducted rapid antigen testing to determine the presence of SARS-CoV-2, and only those with a positive rapid antigen test underwent confirmation testing by RT-PCR. However, participants using NP swab sampling for their self-conducted test could have obtained a false-negative rapid antigen result because of improper collection technique or test performance. However, a distribution of this phenomenon should have occurred similarly in both groups. In addition, the incidence of COVID-19 infection in the prevention study was much lower than we expected. This might be due to several factors, such as the majority of participants in our study have received at least one dose of COVID-19 vaccination, or the changes in the SARSCoV-2 strain from time to time. To detect the difference of the small effect in the prevention study between ivermectin and placebo, more than 3200 participants may be required. Therefore, the result of this prevention study warrants further, larger research.In this double-blinded, pragmatic randomized placebo-controlled trial, ivermectin did not demonstrate a protective effect for preventing SARS-CoV-2 infection. The results also showed that ivermectin had no COVID-19 therapeutic effect in combination with standard of care (favipiravir). Transient blurred vision was significantly more common in participants who received ivermectin plus favipiravir. Therefore, ivermectin should not be used for preventing SARS-CoV-2 infection or for treating mild-to-moderate COVID-19.